PH Deck

Users manually screening hundreds of research papers for systematic reviews face time-consuming and error-prone processes due to the volume and complexity of academic content.

Users spend hours conducting research tasks manually, leading to inefficiency and time-consuming processes.

In the current situation, researchers and academics face challenges in understanding complex research papers and extracting meaningful insights from them. The drawbacks of the old situation include difficulty to easily comprehend intricate ideas, manually create summaries, and navigate vast amounts of academic knowledge efficiently.

Researchers, students, and professionals often struggle with efficiently searching across multiple platforms such as the web, patents, and academic papers for comprehensive information. This process is time-consuming and disjointed, leading to inefficiency in research and difficulty in organizing and retrieving information for later use.

Users struggle with conducting comprehensive research on various subjects due to the need for real-time information and the time-consuming process of writing professional reports, leading to inefficiencies and outdated data.

Users spend significant time manually summarizing academic papers, extracting key insights, and organizing references, leading to inefficiency and potential oversight of critical information.

Researchers and students currently rely on manual annotation and comprehension of academic papers, which is time-consuming and lacks integrated AI assistance. Traditional methods often result in inefficient understanding and verification of complex research content.

Users face difficulties in removing MDM lock, screen passcode, Apple ID, screen time passcode, and iTunes encryption on iPhones, which can be time-consuming and complicated.

Users struggle with traditional academic search methods that rely on keyword-based queries, leading to incomplete or irrelevant paper recommendations and difficulty discovering niche research.

In the current situation, users face challenges in transforming complex data into comprehensible and engaging visual representations. The old solutions often involve manual data analysis and visualization, which can be time-consuming and lack efficiency. The drawbacks of this old situation include the need to manually curate data and the time-intensive process to create insightful visualizations.